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#get sample data
samples.integrated@meta.data %>% as.data.frame() -> samplemeta
spacial_data= samples.microglia@meta.data %>% as.data.frame() %>% select(contains("spatial"))
samplemeta <- cbind(samplemeta, spacial_data[rownames(samplemeta),])
# convert to correct data type
# define genotype as is
samplemeta$Genotype_corr = factor(samplemeta$Genotype=="WT", levels=c(F,T), labels = c("APPPS1+", "WT"))
samplemeta$Genotype_corr = relevel(samplemeta$Genotype_corr, ref="WT")
samplemeta$methoxy = factor(samplemeta$Methoxy=="pos", levels=c(T,F), labels = c("MX04+", "MX04-"))
samplemeta$Treatment = as.factor(samplemeta$Treatment)
samplemeta$Treatment = relevel(samplemeta$Treatment, ref="Ctrl")
samplemeta$Mouse_ID = as.factor(samplemeta$Mouse_ID)
samplemeta$Sex = as.factor(samplemeta$Gender)
samplemeta$Brain_region = as.factor(samplemeta$Brain_region)
samplemeta$Celltype = as.factor(samplemeta$Celltype)
samples.integrated@meta.data = samplemeta
nCells=nrow(samplemeta)
nMice=nlevels(samplemeta$Mouse_ID)
nCelltypes=nlevels(samplemeta$Celltype)
Data contains a total of 562 Cells from 9.
Cells per Mouse
Cells per Strain
table(Mouse_ID=samplemeta$Mouse_ID) %>% as.data.frame() %>% display_tab()
Cells per Genotype
table(Genotype=samplemeta$Genotype_corr, Treatment=samplemeta$Treatment) %>% as.data.frame() %>% display_tab()
table(Genotype=samplemeta$Genotype_corr, Celltype=samplemeta$Celltype) %>%
as.data.frame() %>% display_tab()
## add spatial cell types
table(Genotype=samplemeta$Genotype_corr, Sp_Controls=samplemeta$Control_spatial_binary) %>%
as.data.frame() %>% display_tab()
table(Genotype=samplemeta$Genotype_corr, Sp_Stroke=samplemeta$Stroke_spatial_binary) %>%
as.data.frame() %>% display_tab()
table(Genotype=samplemeta$Genotype_corr,
Celltype=samplemeta$Celltype, Treatment=samplemeta$Treatment
) %>%
as.data.frame() %>% display_tab()
samplemeta$condition=paste0(samplemeta$Genotype_corr,"_", samplemeta$Treatment)
samples.integrated@meta.data = samplemeta
variables=c("Celltype","Sex", "Age","Genotype_corr", "Treatment", "condition","Phase","Control_spatial_binary","Stroke_spatial_binary", "Brain_region","nCount_RNA","pseudoaligned_reads", "percent.mt", "percent.rp", "Mouse_ID")
Descriptive stats across Cell type
res = compareGroups(Celltype~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)
| Microglia_1 | Microglia_2 | Microglia_3 | Microglia_4 | Granulocytes | Microglia_5 | Mono/Mac | p.overall | |
|---|---|---|---|---|---|---|---|---|
| N=372 | N=86 | N=57 | N=8 | N=4 | N=15 | N=20 | ||
| Sex: | 0.276 | |||||||
| f | 73 (19.6%) | 20 (23.3%) | 17 (29.8%) | 2 (25.0%) | 2 (50.0%) | 5 (33.3%) | 5 (25.0%) | |
| m | 299 (80.4%) | 66 (76.7%) | 40 (70.2%) | 6 (75.0%) | 2 (50.0%) | 10 (66.7%) | 15 (75.0%) | |
| Age: | . | |||||||
| 37 weeks | 226 (60.8%) | 56 (65.1%) | 41 (71.9%) | 6 (75.0%) | 2 (50.0%) | 7 (46.7%) | 11 (55.0%) | |
| 38 weeks | 44 (11.8%) | 8 (9.30%) | 13 (22.8%) | 2 (25.0%) | 2 (50.0%) | 4 (26.7%) | 3 (15.0%) | |
| 40 weeks | 102 (27.4%) | 22 (25.6%) | 3 (5.26%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 6 (30.0%) | |
| Genotype_corr: | . | |||||||
| WT | 170 (45.7%) | 43 (50.0%) | 42 (73.7%) | 8 (100%) | 4 (100%) | 8 (53.3%) | 15 (75.0%) | |
| APPPS1+ | 202 (54.3%) | 43 (50.0%) | 15 (26.3%) | 0 (0.00%) | 0 (0.00%) | 7 (46.7%) | 5 (25.0%) | |
| Treatment: | . | |||||||
| Ctrl | 272 (73.1%) | 56 (65.1%) | 52 (91.2%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (40.0%) | |
| Stroke | 100 (26.9%) | 30 (34.9%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 12 (60.0%) | |
| condition: | . | |||||||
| APPPS1+_Ctrl | 164 (44.1%) | 28 (32.6%) | 10 (17.5%) | 0 (0.00%) | 0 (0.00%) | 6 (40.0%) | 1 (5.00%) | |
| APPPS1+_Stroke | 38 (10.2%) | 15 (17.4%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 1 (6.67%) | 4 (20.0%) | |
| WT_Ctrl | 108 (29.0%) | 28 (32.6%) | 42 (73.7%) | 8 (100%) | 4 (100%) | 5 (33.3%) | 7 (35.0%) | |
| WT_Stroke | 62 (16.7%) | 15 (17.4%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3 (20.0%) | 8 (40.0%) | |
| Phase: | . | |||||||
| G1 | 159 (42.7%) | 36 (41.9%) | 19 (33.3%) | 0 (0.00%) | 2 (50.0%) | 10 (66.7%) | 9 (45.0%) | |
| G2M | 95 (25.5%) | 18 (20.9%) | 17 (29.8%) | 2 (25.0%) | 2 (50.0%) | 2 (13.3%) | 6 (30.0%) | |
| S | 118 (31.7%) | 32 (37.2%) | 21 (36.8%) | 6 (75.0%) | 0 (0.00%) | 3 (20.0%) | 5 (25.0%) | |
| Control_spatial_binary: | 0.080 | |||||||
| FALSE | 338 (91.1%) | 75 (87.2%) | 57 (100%) | 8 (100%) | 4 (100%) | 15 (100%) | 8 (100%) | |
| TRUE | 33 (8.89%) | 11 (12.8%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | |
| Stroke_spatial_binary: | . | |||||||
| FALSE | 240 (64.7%) | 55 (64.0%) | 53 (93.0%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (100%) | |
| TRUE | 131 (35.3%) | 31 (36.0%) | 4 (7.02%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 0 (0.00%) | |
| Brain_region: | . | |||||||
| Cortex | 272 (73.1%) | 56 (65.1%) | 52 (91.2%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (40.0%) | |
| Lesion | 100 (26.9%) | 30 (34.9%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 12 (60.0%) | |
| nCount_RNA | 158416 (103720) | 194411 (106729) | 164921 (154435) | 217767 (205467) | 204215 (86217) | 165673 (126013) | 162375 (90426) | 0.154 |
| pseudoaligned_reads | 158947 (104087) | 194836 (106825) | 165764 (154492) | 218138 (205598) | 204425 (86225) | 166053 (126105) | 163005 (90604) | 0.160 |
| percent.mt | 1.90 (1.35) | 2.14 (1.78) | 1.44 (1.07) | 1.66 (1.04) | 0.98 (1.13) | 2.01 (1.01) | 2.04 (1.27) | 0.089 |
| percent.rp | 2.21 (1.29) | 3.05 (2.17) | 1.48 (1.34) | 0.98 (0.41) | 1.02 (0.50) | 2.03 (1.25) | 3.16 (2.30) | <0.001 |
| Mouse_ID: | . | |||||||
| 256#1022 | 33 (8.87%) | 9 (10.5%) | 13 (22.8%) | 2 (25.0%) | 0 (0.00%) | 1 (6.67%) | 2 (10.0%) | |
| 256#1023 | 31 (8.33%) | 11 (12.8%) | 16 (28.1%) | 4 (50.0%) | 2 (50.0%) | 0 (0.00%) | 2 (10.0%) | |
| 364#469 | 44 (11.8%) | 8 (9.30%) | 13 (22.8%) | 2 (25.0%) | 2 (50.0%) | 4 (26.7%) | 3 (15.0%) | |
| 386 | 16 (4.30%) | 4 (4.65%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 4 (20.0%) | |
| 387 | 9 (2.42%) | 3 (3.49%) | 1 (1.75%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 2 (10.0%) | |
| 388 | 77 (20.7%) | 15 (17.4%) | 2 (3.51%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 0 (0.00%) | |
| 409 | 46 (12.4%) | 11 (12.8%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3 (20.0%) | 4 (20.0%) | |
| 457 | 87 (23.4%) | 13 (15.1%) | 8 (14.0%) | 0 (0.00%) | 0 (0.00%) | 2 (13.3%) | 1 (5.00%) | |
| 461 | 29 (7.80%) | 12 (14.0%) | 4 (7.02%) | 0 (0.00%) | 0 (0.00%) | 1 (6.67%) | 2 (10.0%) |
export2xls(export_table,paste0(home,"/docs/Descriptives.xlsx"))
## compare Binary Spatial Controls
res = compareGroups(Control_spatial_binary~., data = samplemeta[,variables], max.ylev = 10)
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
Warning in chisq.test(xx, correct = FALSE): Chi-squared approximation may be incorrect
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)
| FALSE | TRUE | p.overall | |
|---|---|---|---|
| N=505 | N=44 | ||
| Celltype: | 0.080 | ||
| Microglia_1 | 338 (66.9%) | 33 (75.0%) | |
| Microglia_2 | 75 (14.9%) | 11 (25.0%) | |
| Microglia_3 | 57 (11.3%) | 0 (0.00%) | |
| Microglia_4 | 8 (1.58%) | 0 (0.00%) | |
| Granulocytes | 4 (0.79%) | 0 (0.00%) | |
| Microglia_5 | 15 (2.97%) | 0 (0.00%) | |
| Mono/Mac | 8 (1.58%) | 0 (0.00%) | |
| Sex: | 0.046 | ||
| f | 118 (23.4%) | 4 (9.09%) | |
| m | 387 (76.6%) | 40 (90.9%) | |
| Age: | 0.019 | ||
| 37 weeks | 316 (62.6%) | 27 (61.4%) | |
| 38 weeks | 75 (14.9%) | 1 (2.27%) | |
| 40 weeks | 114 (22.6%) | 16 (36.4%) | |
| Genotype_corr: | <0.001 | ||
| WT | 274 (54.3%) | 9 (20.5%) | |
| APPPS1+ | 231 (45.7%) | 35 (79.5%) | |
| Treatment: | 0.795 | ||
| Ctrl | 375 (74.3%) | 34 (77.3%) | |
| Stroke | 130 (25.7%) | 10 (22.7%) | |
| condition: | <0.001 | ||
| APPPS1+_Ctrl | 178 (35.2%) | 29 (65.9%) | |
| APPPS1+_Stroke | 53 (10.5%) | 6 (13.6%) | |
| WT_Ctrl | 197 (39.0%) | 5 (11.4%) | |
| WT_Stroke | 77 (15.2%) | 4 (9.09%) | |
| Phase: | 0.215 | ||
| G1 | 217 (43.0%) | 13 (29.5%) | |
| G2M | 123 (24.4%) | 14 (31.8%) | |
| S | 165 (32.7%) | 17 (38.6%) | |
| Stroke_spatial_binary: | <0.001 | ||
| FALSE | 371 (73.5%) | 8 (18.2%) | |
| TRUE | 134 (26.5%) | 36 (81.8%) | |
| Brain_region: | 0.795 | ||
| Cortex | 375 (74.3%) | 34 (77.3%) | |
| Lesion | 130 (25.7%) | 10 (22.7%) | |
| nCount_RNA | 161418 (112945) | 214465 (106144) | 0.003 |
| pseudoaligned_reads | 161849 (113014) | 214704 (106166) | 0.003 |
| percent.mt | 1.76 (1.27) | 3.14 (2.04) | <0.001 |
| percent.rp | 2.05 (1.34) | 4.20 (2.00) | <0.001 |
| Mouse_ID: | . | ||
| 256#1022 | 58 (11.5%) | 2 (4.55%) | |
| 256#1023 | 64 (12.7%) | 2 (4.55%) | |
| 364#469 | 75 (14.9%) | 1 (2.27%) | |
| 386 | 19 (3.76%) | 1 (2.27%) | |
| 387 | 10 (1.98%) | 3 (6.82%) | |
| 388 | 85 (16.8%) | 12 (27.3%) | |
| 409 | 58 (11.5%) | 3 (6.82%) | |
| 457 | 93 (18.4%) | 17 (38.6%) | |
| 461 | 43 (8.51%) | 3 (6.82%) |
export2xls(export_table,paste0(home,"/docs/Descriptives_sp_Control.xlsx"))
## compare Binary Spatial Stroke
res = compareGroups(Stroke_spatial_binary~., data = samplemeta[,variables], max.ylev = 10)
#summary(res)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)
| FALSE | TRUE | p.overall | |
|---|---|---|---|
| N=379 | N=170 | ||
| Celltype: | . | ||
| Microglia_1 | 240 (63.3%) | 131 (77.1%) | |
| Microglia_2 | 55 (14.5%) | 31 (18.2%) | |
| Microglia_3 | 53 (14.0%) | 4 (2.35%) | |
| Microglia_4 | 8 (2.11%) | 0 (0.00%) | |
| Granulocytes | 4 (1.06%) | 0 (0.00%) | |
| Microglia_5 | 11 (2.90%) | 4 (2.35%) | |
| Mono/Mac | 8 (2.11%) | 0 (0.00%) | |
| Sex: | <0.001 | ||
| f | 103 (27.2%) | 19 (11.2%) | |
| m | 276 (72.8%) | 151 (88.8%) | |
| Age: | <0.001 | ||
| 37 weeks | 242 (63.9%) | 101 (59.4%) | |
| 38 weeks | 76 (20.1%) | 0 (0.00%) | |
| 40 weeks | 61 (16.1%) | 69 (40.6%) | |
| Genotype_corr: | <0.001 | ||
| WT | 249 (65.7%) | 34 (20.0%) | |
| APPPS1+ | 130 (34.3%) | 136 (80.0%) | |
| Treatment: | 0.003 | ||
| Ctrl | 297 (78.4%) | 112 (65.9%) | |
| Stroke | 82 (21.6%) | 58 (34.1%) | |
| condition: | <0.001 | ||
| APPPS1+_Ctrl | 97 (25.6%) | 110 (64.7%) | |
| APPPS1+_Stroke | 33 (8.71%) | 26 (15.3%) | |
| WT_Ctrl | 200 (52.8%) | 2 (1.18%) | |
| WT_Stroke | 49 (12.9%) | 32 (18.8%) | |
| Phase: | 0.573 | ||
| G1 | 155 (40.9%) | 75 (44.1%) | |
| G2M | 93 (24.5%) | 44 (25.9%) | |
| S | 131 (34.6%) | 51 (30.0%) | |
| Control_spatial_binary: | <0.001 | ||
| FALSE | 371 (97.9%) | 134 (78.8%) | |
| TRUE | 8 (2.11%) | 36 (21.2%) | |
| Brain_region: | 0.003 | ||
| Cortex | 297 (78.4%) | 112 (65.9%) | |
| Lesion | 82 (21.6%) | 58 (34.1%) | |
| nCount_RNA | 161384 (122017) | 175222 (90282) | 0.139 |
| pseudoaligned_reads | 161834 (122084) | 175563 (90328) | 0.143 |
| percent.mt | 1.54 (1.03) | 2.62 (1.78) | <0.001 |
| percent.rp | 1.59 (1.06) | 3.62 (1.46) | <0.001 |
| Mouse_ID: | . | ||
| 256#1022 | 58 (15.3%) | 2 (1.18%) | |
| 256#1023 | 66 (17.4%) | 0 (0.00%) | |
| 364#469 | 76 (20.1%) | 0 (0.00%) | |
| 386 | 8 (2.11%) | 12 (7.06%) | |
| 387 | 6 (1.58%) | 7 (4.12%) | |
| 388 | 47 (12.4%) | 50 (29.4%) | |
| 409 | 41 (10.8%) | 20 (11.8%) | |
| 457 | 50 (13.2%) | 60 (35.3%) | |
| 461 | 27 (7.12%) | 19 (11.2%) |
export2xls(export_table,paste0(home,"/docs/Descriptives_sp_Stroke.xlsx"))
download data as excel file here
include only genes that are different across samples (5 974 excluded) include only genes with more than 10 reads in at least 10 cells in at least one Celltype (21 733 transcripts excluded) exclude all cells with less than 10000 reads (none excluded)
649 cells and 11 623 transcripts analyzed
#get normalized counts
# question to Desiree hat the Seurat object been initialized with normalized data?
counts <- samples.integrated@assays$RNA@counts %>% as.data.frame()
# drop no variance data and sort by samplemeta
counts <- counts[apply(counts,1, sd) > 0, rownames(samplemeta)]
# drop genes with low detection rate (more than 5 counts per cell)
counts_per_celltype=apply(counts, 1, function(x){tapply(x, samplemeta$Celltype, function(z){sum(z>10,na.rm=T)})}) %>% as.data.frame()
# keep RNAs with at least 10 cells with good expression
idxr=which(colSums(counts_per_celltype)>10)
idxc=which(colSums(counts)>10000)
counts = counts[idxr,idxc]
samplemeta=samplemeta[colnames(counts), ]
samplemeta$corrGenotype_Treatment<-
paste0(samplemeta$Genotype_corr," ", samplemeta$Treatment)
sumstat = counts %>% t() %>%
aggregate(by=samplemeta["corrGenotype_Treatment"], mean) %>%
t() %>% as.data.frame()
names(sumstat) <- paste0("mean",sumstat[1,])
sumstat<-sumstat[-1,]
sumstatCelltype = counts %>% t() %>%
aggregate(by=samplemeta["Celltype"], mean) %>%
t() %>% as.data.frame()
names(sumstatCelltype) <- paste0("mean",sumstatCelltype[1,])
sumstatCelltype<-sumstatCelltype[-1,]
sumstat_spStroke = counts %>% t() %>%
aggregate(by=samplemeta["Stroke_spatial_binary"], mean) %>%
t() %>% as.data.frame()
names(sumstat_spStroke) <- paste0("mean_spStrk",sumstat_spStroke[1,])
sumstat_spStroke<-sumstat_spStroke[-1,]
sumstat_spCtrl = counts %>% t() %>%
aggregate(by=samplemeta["Control_spatial_binary"], mean) %>%
t() %>% as.data.frame()
names(sumstat_spCtrl) <- paste0("mean_spStrk",sumstat_spCtrl[1,])
sumstat_spCtrl<-sumstat_spCtrl[-1,]
Geno.Treatment_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$corrGenotype_Treatment) %>% unlist()
return(as.numeric(res["p.value"]))
})
Geno.Treatment_kruskal_fdr = p.adjust(Geno.Treatment_kruskal, method = "fdr")
Celltype_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$Celltype) %>% unlist()
return(as.numeric(res["p.value"]))
})
Celltype_kruskal_fdr = p.adjust(Celltype_kruskal, method = "fdr")
Control_spatial_binary_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$Control_spatial_binary) %>% unlist()
return(as.numeric(res["p.value"]))
})
Control_spatial_binary_kruskal_fdr = p.adjust(Control_spatial_binary_kruskal, method = "fdr")
Stroke_spatial_binary_kruskal=apply(counts,1, function(x){
res = kruskal.test(unlist(x)~samplemeta$Stroke_spatial_binary) %>% unlist()
return(as.numeric(res["p.value"]))
})
Stroke_spatial_binary_kruskal_fdr = p.adjust(Stroke_spatial_binary_kruskal, method = "fdr")
summarytab=cbind(sumstat, Geno.Treatment_kruskal, Geno.Treatment_kruskal_fdr ,
sumstatCelltype, Celltype_kruskal, Celltype_kruskal_fdr,
sumstat_spCtrl, Control_spatial_binary_kruskal, Control_spatial_binary_kruskal_fdr,
sumstat_spStroke,Stroke_spatial_binary_kruskal,Stroke_spatial_binary_kruskal_fdr)
summarytab %>% display_tab()
Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/DT/server.html
to check where the variance in the data comes from
log2_cpm = log2(counts+1)
varsset=apply(log2_cpm, 1, var)
cpm.sel.trans = t(log2_cpm[order(varsset,decreasing = T)[1:1000],])
distance = dist(cpm.sel.trans)
sampleDistMatrix <- as.matrix(distance)
#colors for plotting heatmap
colors <- rev(colorRampPalette(brewer.pal(9, "Spectral"))(255))
colors=jetcolors(255)
colors=viridis(255)
cellcol = Dark8[1:nlevels(samplemeta$Celltype)]
names(cellcol) = levels(samplemeta$Celltype)
Stroke_sp = Dark8[1:nlevels(samplemeta$Stroke_spatial_binary)]
names(Stroke_sp) = levels(samplemeta$Stroke_spatial_binary)
Ctrl_sp = Dark8[1:nlevels(samplemeta$Control_spatial_binary)]
names(Ctrl_sp) = levels(samplemeta$Control_spatial_binary)
genotypecol = brewer.pal(4,"Accent")[c(1:nlevels(samplemeta$Genotype_corr))]
names(genotypecol) = levels(samplemeta$Genotype_corr)
strokecol = brewer.pal(5,"Set2")[1:nlevels(samplemeta$Treatment)+2]
names(strokecol) = levels(samplemeta$Treatment)
mousecol = brewer.pal(9,"Set1")[1:nlevels(samplemeta$Mouse_ID)]
names(mousecol) = levels(samplemeta$Mouse_ID)
braincol = brewer.pal(3,"Set2")[1:nlevels(samplemeta$Brain_region)]
names(braincol) = levels(samplemeta$Brain_region)
samplemeta$Age = as.factor(samplemeta$Age)
Agecol = colorRampPalette(c("dodgerblue",
"dodgerblue4"))(nlevels(samplemeta$Age))[1:nlevels(samplemeta$Age)]
names(Agecol) = levels(samplemeta$Age)
ann_colors = list(
Genotype_corr = genotypecol,
Mouse_ID = mousecol,
Brain_region = braincol,
Celltype=cellcol,
Treatment=strokecol,
Age=Agecol,
Control_spatial_binary = Ctrl_sp,
Stroke_spatial_binary =Stroke_sp)
labels = samplemeta[,c("Genotype_corr","Mouse_ID", "Age", "Brain_region", "Celltype",
"Control_spatial_binary","Stroke_spatial_binary","Treatment")] %>%
mutate_all(as.character) %>% as.data.frame()
rownames(labels)=rownames(samplemeta)
pheatmap(sampleDistMatrix,
clustering_distance_rows = distance,
clustering_distance_cols = distance,
clustering_method = "ward.D2",
scale ="none",
legend = F,
show_rownames=F, show_colnames = F,
border_color = NA,
annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
main = "D62 top1000 Distances normalized log2 counts")

| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 51b35c8 | achiocch | 2022-04-26 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| f21fdf7 | achiocch | 2022-03-30 |
| 858d646 | achiocch | 2022-02-03 |

the logFC should not be interpreted on its own without the specific post hoc tests (see app below to check the individual genes) In any case the value here would correspond to the APPPS1+ with Stroke against all others.
Heatmaps show standardized deviations from mean across all cells, trimmed to 2 standard deviations ( i.e. values above 2 SD are set to 2 SD). this allows to see more subtle changes better.
getres=function(Celltype="Specify",
Hypothesis="~1+Sex+Genotype_corr*Treatment",
Target="Genotype_corrAPPPS1+:TreatmentStroke",
Randomeffect=NULL){
res= comparison_rand(designform =Hypothesis,
randomeffect = Randomeffect,
Samples = samplemeta$Celltype==Celltype,
log_cpm = log2_cpm,
samplesdata = samplemeta,
target=Target)
labels = samplemeta[samplemeta$Celltype==Celltype,c("Treatment","Age", "Genotype_corr","Mouse_ID", "Brain_region", "Celltype","Control_spatial_binary", "Stroke_spatial_binary")] %>%
mutate_all(as.character) %>% as.data.frame()
labels=labels %>% arrange(Treatment,Genotype_corr)
plotdata=log2_cpm[res$adj.P.Val<0.05,rownames(labels)]
plotdata = apply(plotdata, 2, trimmed_scaled)
pheatmap(plotdata,
#clustering_method = "ward.D2",
cluster_cols = F,
cluster_rows = T,
scale ="row",
show_rownames=F, show_colnames = F,
legend=T,
border_color = NA,
#annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
breaks = seq(-2,2, length.out=254),
main = "D62 Distances normalized log2 counts")
return(res)
}
getresspatio=function(Celltype="Specify",
Hypothesis="~1+Sex+Genotype_corr*Treatment",
Target="Genotype_corrAPPPS1+:TreatmentStroke",
Randomeffect=NULL){
idx=NA
if(Celltype =="Stroke_spatial_binary"){
idx=samplemeta$Stroke_spatial_binary==T
}
if(Celltype =="Control_spatial_binary"){
idx=samplemeta$Control_spatial_binary==T
}
idx[is.na(idx)]=F
res=comparison_rand(designform =Hypothesis,
randomeffect = Randomeffect,
Samples = idx,
log_cpm = log2_cpm,
samplesdata = samplemeta,
target=Target)
labels = samplemeta[idx,c("Treatment","Age", "Genotype_corr","Mouse_ID", "Brain_region", "Celltype", "Control_spatial_binary", "Stroke_spatial_binary")] %>%
mutate_all(as.character) %>% as.data.frame()
labels=labels %>% arrange(Treatment,Genotype_corr)
plotdata=log2_cpm[res$adj.P.Val<0.05,rownames(labels)]
plotdata = apply(plotdata, 2, trimmed_scaled)
pheatmap(plotdata,
#clustering_method = "ward.D2",
cluster_cols = F,
cluster_rows = T,
scale ="row",
show_rownames=F, show_colnames = F,
legend=T,
border_color = NA,
#annotation_row = labels,
annotation_col = labels,
annotation_colors = ann_colors,
col = colors,
breaks = seq(-2,2, length.out=254),
main = "D62 Distances normalized log2 counts")
return(res)
}
generate_output=function(CT="Celltype",mode="Celltype", ...){
respath=paste0(home, "/docs/LMER_",CT,".xlsx")
if(mode == "Spatial"){
analysis = getresspatio(Celltype = CT)
}
if (mode == "Celltype"){
analysis = getres(Celltype = CT)}
analysis_sig = analysis[analysis$adj.P.Val<0.05,]
analysis_sig %>% display_tab()
write.xlsx2(analysis_sig, file =respath , sheetName = "significant genes")
ResGO = getGOresults(rownames(analysis_sig),
rownames(analysis),
"mmusculus")
if(length(ResGO)>0){
p=gostplot(ResGO)
} else{
ResGO=data.frame(result="no significant enrichment identified")
p="no significant enrichment identified"
}
write.xlsx2(ResGO$result, file = respath, sheetName = "GO_enrichment", append=T)
return(list(results=analysis,results_sig=analysis_sig, plot=p))
}
res_output=generate_output(CT = "Microglia_1")

| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| 87439a3 | achiocch | 2022-02-21 |
| 79dd0b1 | achiocch | 2022-02-21 |
| 858d646 | achiocch | 2022-02-03 |
[1] "no significant GO terms identified"
res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]
[1] "no significant enrichment identified"
Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
res_output=generate_output("Microglia_2")

| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| 87439a3 | achiocch | 2022-02-21 |
| 79dd0b1 | achiocch | 2022-02-21 |
| 858d646 | achiocch | 2022-02-03 |
[1] "no significant GO terms identified"
res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]
[1] "no significant enrichment identified"
Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
idx=samplemeta$Celltype=="Microglia_3"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 42 10
Stroke 0 5
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 0.0007172
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
3.138602 Inf
sample estimates:
odds ratio
Inf
Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA)
get results table here
calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference
idx=samplemeta$Celltype=="Microglia_4"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 7 0
Stroke 0 0
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0 Inf
sample estimates:
odds ratio
0
#res_output=generate_output("Microglia_4")
#res_output[["results_sig"]] %>% display_tab()
#res_output[["plot"]]
get results table here
res_output=generate_output("Microglia_5")

| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
| 982b9f6 | Andreas Chiocchetti | 2023-02-20 |
| eaa373d | Andreas Chiocchetti | 2023-02-16 |
| cfbfcf6 | achiocch | 2022-06-22 |
| 8e44cfc | achiocch | 2022-04-22 |
| 55e7738 | achiocch | 2022-03-30 |
| 87439a3 | achiocch | 2022-02-21 |
| 79dd0b1 | achiocch | 2022-02-21 |
| 858d646 | achiocch | 2022-02-03 |
[1] "no significant GO terms identified"
res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]
[1] "no significant enrichment identified"
Data sources and their abbreviations are: Gene Ontology (GO or by branch GO:MF, GO:BP, GO:CC)KEGG (KEGG) TRANSFAC (TF) miRTarBase (MIRNA) CORUM (CORUM) Human phenotype ontology (HP) Human Protein Atlas (HPA) get results table here
calculation not converging as Celltype not identified in all conditions however also rare in other conditions, no significant difference
idx=samplemeta$Celltype=="Granulocytes"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 4 0
Stroke 0 0
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0 Inf
sample estimates:
odds ratio
0
# res_output=generate_output("Granulocytes ")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]
get results table here
idx=samplemeta$Celltype=="Mono/Mac"
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx])
WT APPPS1+
Ctrl 7 1
Stroke 8 4
table(samplemeta$Treatment[idx], samplemeta$Genotype_corr[idx]) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 0.6027
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2433699 196.3003974
sample estimates:
odds ratio
3.302108
# res_output=generate_output("T/NK")
# res_output[["results_sig"]] %>% display_tab()
# res_output[["plot"]]
get results table here
res_output=generate_output(CT = "Control_spatial_binary", mode="Spatial")

| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
[1] "no significant GO terms identified"
res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]
[1] "no significant enrichment identified"
res_output=generate_output("Stroke_spatial_binary", mode="Spatial")

| Version | Author | Date |
|---|---|---|
| d5c8125 | Andreas Chiocchetti | 2023-07-14 |
[1] "no significant GO terms identified"
res_output[["results_sig"]] %>% display_tab()
res_output[["plot"]]
[1] "no significant enrichment identified"
Riddgplot suggests Microglia_4 as the homeostatic one
samples.microglia = samples.integrated[,grepl("Microgl", samples.integrated$Celltype)]
RidgePlot(samples.microglia, features = c("Tmem119", "Cx3cr1", "P2ry12", "P2ry13", "Axl", "Itgax", "Cst7", "Clec7a", "Spp1"), ncol = 2)
Picking joint bandwidth of 0.581
Picking joint bandwidth of 0.247
Picking joint bandwidth of 0.441
Picking joint bandwidth of 0.598
Picking joint bandwidth of 0.431
Picking joint bandwidth of 0.0287
Picking joint bandwidth of 0.601
Picking joint bandwidth of 0.524
Picking joint bandwidth of 5.04e-06

| Version | Author | Date |
|---|---|---|
| 9d29eff | Andreas Chiocchetti | 2023-08-11 |
samplemeta.microglia = samples.microglia@meta.data
sce.microglia=as.SingleCellExperiment(samples.microglia)
sce.microglia <- slingshot(sce.microglia, clusterLabels = 'Celltype', reducedDim = 'UMAP', start.clus="Microglia_4")
samplemeta.microglia$Pseudotime_1 <- sce.microglia$slingPseudotime_1
samplemeta.microglia$Pseudotime_2 <- sce.microglia$slingPseudotime_2
dataset = data.frame(reducedDim(sce.microglia, "UMAP"),
cluster = as.character(sce.microglia$Celltype),
Stroke_spatial = as.character(sce.microglia$Stroke_spatial_binary))
curves <- slingCurves(sce.microglia, as.df=T)
p = ggplot(dataset, aes(x=UMAP_1, y=UMAP_2))+
geom_point(aes(col = cluster), size=2)+
geom_point(data=subset(dataset, dataset$Stroke_spatial==T), aes(fill=Stroke_spatial), pch=21, size=0.7)+
scale_fill_manual(values="black")
curves = curves %>% arrange(Lineage, Order)
p = p + geom_path(data = curves, aes(group = Lineage), lwd=1.5, lineend = "butt",)+theme_classic()
curves_arrows = curves %>% group_by(Lineage) %>% mutate(xend = lead(UMAP_1), yend = lead(UMAP_2))
curves_arrows = curves_arrows[curves_arrows$Order %in% seq(10,150, by=floor((150-10)/10)),]
p = p + geom_segment(data = curves_arrows,aes(x = UMAP_1, y = UMAP_2, xend = xend, yend = yend), arrow = arrow(length=unit(0.3, "cm"), type="closed"))
p
Warning: Removed 2 rows containing missing values (`geom_segment()`).

ggsave(filename = "./docs/Pseudotime_linages.svg", p)
Saving 6 x 6 in image
Warning: Removed 2 rows containing missing values (`geom_segment()`).
ggsave(filename = "./docs/Pseudotime_linages.pdf", p)
Saving 6 x 6 in image
Warning: Removed 2 rows containing missing values (`geom_segment()`).
dataset$pseudotime_1 <- sce.microglia$slingPseudotime_1
dataset$pseudotime_2 <- sce.microglia$slingPseudotime_2
dataset$Genotype_Treatment <- sce.microglia$Genotype_Treatment
metadata = samples.integrated@meta.data %>% as.data.frame()
metadata$Pseudotime_1=NA
metadata$Pseudotime_2=NA
metadata[rownames(dataset),]$Pseudotime_1 = dataset$pseudotime_1
metadata[rownames(dataset),]$Pseudotime_2 = dataset$pseudotime_2
samples.integrated@meta.data <- metadata
linages = sce.microglia %>% slingLineages()
res = compareGroups(Celltype~., data = metadata[,c(variables, "Pseudotime_1", "Pseudotime_2")], max.ylev = 10)
export_table <- createTable(res)
options(width = 10000)
export2md(export_table)
| Microglia_1 | Microglia_2 | Microglia_3 | Microglia_4 | Granulocytes | Microglia_5 | Mono/Mac | p.overall | |
|---|---|---|---|---|---|---|---|---|
| N=372 | N=86 | N=57 | N=8 | N=4 | N=15 | N=20 | ||
| Sex: | 0.276 | |||||||
| f | 73 (19.6%) | 20 (23.3%) | 17 (29.8%) | 2 (25.0%) | 2 (50.0%) | 5 (33.3%) | 5 (25.0%) | |
| m | 299 (80.4%) | 66 (76.7%) | 40 (70.2%) | 6 (75.0%) | 2 (50.0%) | 10 (66.7%) | 15 (75.0%) | |
| Age: | . | |||||||
| 37 weeks | 226 (60.8%) | 56 (65.1%) | 41 (71.9%) | 6 (75.0%) | 2 (50.0%) | 7 (46.7%) | 11 (55.0%) | |
| 38 weeks | 44 (11.8%) | 8 (9.30%) | 13 (22.8%) | 2 (25.0%) | 2 (50.0%) | 4 (26.7%) | 3 (15.0%) | |
| 40 weeks | 102 (27.4%) | 22 (25.6%) | 3 (5.26%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 6 (30.0%) | |
| Genotype_corr: | . | |||||||
| WT | 170 (45.7%) | 43 (50.0%) | 42 (73.7%) | 8 (100%) | 4 (100%) | 8 (53.3%) | 15 (75.0%) | |
| APPPS1+ | 202 (54.3%) | 43 (50.0%) | 15 (26.3%) | 0 (0.00%) | 0 (0.00%) | 7 (46.7%) | 5 (25.0%) | |
| Treatment: | . | |||||||
| Ctrl | 272 (73.1%) | 56 (65.1%) | 52 (91.2%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (40.0%) | |
| Stroke | 100 (26.9%) | 30 (34.9%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 12 (60.0%) | |
| condition: | . | |||||||
| APPPS1+_Ctrl | 164 (44.1%) | 28 (32.6%) | 10 (17.5%) | 0 (0.00%) | 0 (0.00%) | 6 (40.0%) | 1 (5.00%) | |
| APPPS1+_Stroke | 38 (10.2%) | 15 (17.4%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 1 (6.67%) | 4 (20.0%) | |
| WT_Ctrl | 108 (29.0%) | 28 (32.6%) | 42 (73.7%) | 8 (100%) | 4 (100%) | 5 (33.3%) | 7 (35.0%) | |
| WT_Stroke | 62 (16.7%) | 15 (17.4%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3 (20.0%) | 8 (40.0%) | |
| Phase: | . | |||||||
| G1 | 159 (42.7%) | 36 (41.9%) | 19 (33.3%) | 0 (0.00%) | 2 (50.0%) | 10 (66.7%) | 9 (45.0%) | |
| G2M | 95 (25.5%) | 18 (20.9%) | 17 (29.8%) | 2 (25.0%) | 2 (50.0%) | 2 (13.3%) | 6 (30.0%) | |
| S | 118 (31.7%) | 32 (37.2%) | 21 (36.8%) | 6 (75.0%) | 0 (0.00%) | 3 (20.0%) | 5 (25.0%) | |
| Control_spatial_binary: | 0.080 | |||||||
| FALSE | 338 (91.1%) | 75 (87.2%) | 57 (100%) | 8 (100%) | 4 (100%) | 15 (100%) | 8 (100%) | |
| TRUE | 33 (8.89%) | 11 (12.8%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | |
| Stroke_spatial_binary: | . | |||||||
| FALSE | 240 (64.7%) | 55 (64.0%) | 53 (93.0%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (100%) | |
| TRUE | 131 (35.3%) | 31 (36.0%) | 4 (7.02%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 0 (0.00%) | |
| Brain_region: | . | |||||||
| Cortex | 272 (73.1%) | 56 (65.1%) | 52 (91.2%) | 8 (100%) | 4 (100%) | 11 (73.3%) | 8 (40.0%) | |
| Lesion | 100 (26.9%) | 30 (34.9%) | 5 (8.77%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 12 (60.0%) | |
| nCount_RNA | 158416 (103720) | 194411 (106729) | 164921 (154435) | 217767 (205467) | 204215 (86217) | 165673 (126013) | 162375 (90426) | 0.154 |
| pseudoaligned_reads | 158947 (104087) | 194836 (106825) | 165764 (154492) | 218138 (205598) | 204425 (86225) | 166053 (126105) | 163005 (90604) | 0.160 |
| percent.mt | 1.90 (1.35) | 2.14 (1.78) | 1.44 (1.07) | 1.66 (1.04) | 0.98 (1.13) | 2.01 (1.01) | 2.04 (1.27) | 0.089 |
| percent.rp | 2.21 (1.29) | 3.05 (2.17) | 1.48 (1.34) | 0.98 (0.41) | 1.02 (0.50) | 2.03 (1.25) | 3.16 (2.30) | <0.001 |
| Mouse_ID: | . | |||||||
| 256#1022 | 33 (8.87%) | 9 (10.5%) | 13 (22.8%) | 2 (25.0%) | 0 (0.00%) | 1 (6.67%) | 2 (10.0%) | |
| 256#1023 | 31 (8.33%) | 11 (12.8%) | 16 (28.1%) | 4 (50.0%) | 2 (50.0%) | 0 (0.00%) | 2 (10.0%) | |
| 364#469 | 44 (11.8%) | 8 (9.30%) | 13 (22.8%) | 2 (25.0%) | 2 (50.0%) | 4 (26.7%) | 3 (15.0%) | |
| 386 | 16 (4.30%) | 4 (4.65%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 4 (20.0%) | |
| 387 | 9 (2.42%) | 3 (3.49%) | 1 (1.75%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 2 (10.0%) | |
| 388 | 77 (20.7%) | 15 (17.4%) | 2 (3.51%) | 0 (0.00%) | 0 (0.00%) | 4 (26.7%) | 0 (0.00%) | |
| 409 | 46 (12.4%) | 11 (12.8%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3 (20.0%) | 4 (20.0%) | |
| 457 | 87 (23.4%) | 13 (15.1%) | 8 (14.0%) | 0 (0.00%) | 0 (0.00%) | 2 (13.3%) | 1 (5.00%) | |
| 461 | 29 (7.80%) | 12 (14.0%) | 4 (7.02%) | 0 (0.00%) | 0 (0.00%) | 1 (6.67%) | 2 (10.0%) | |
| Pseudotime_1 | 10.7 (1.86) | 5.17 (1.34) | 3.89 (0.17) | 0.48 (0.48) | . (.) | 14.2 (0.29) | . (.) | <0.001 |
| Pseudotime_2 | 8.08 (1.90) | 5.46 (1.18) | 8.94 (1.36) | 0.48 (0.48) | . (.) | . (.) | . (.) | <0.001 |
export2xls(export_table,paste0(home,"/docs/Descriptives_withPseudotime.xlsx"))
p1 <-ggplot(dataset, aes(x=pseudotime_1))+
geom_density(aes(group=Genotype_Treatment, col=Genotype_Treatment), lwd=2)+
theme_classic()
p = ggplot(dataset, aes(x=UMAP_1, y=UMAP_2))+
geom_point(aes(col = pseudotime_1), size=4)+
geom_point(data=subset(dataset, dataset$Stroke_spatial==T), aes(fill=Stroke_spatial), pch=21)+
scale_fill_manual(values="black")+scale_color_viridis()+theme_classic()
p = p + geom_path(data = curves[curves$Lineage==1,] %>% arrange(Order), aes(group = Lineage), lwd=2)+theme_classic()
p = p + geom_segment(data = curves_arrows[curves_arrows$Lineage==1,],aes(x = UMAP_1, y = UMAP_2, xend = xend, yend = yend), arrow = arrow(length=unit(0.3, "cm"), type="closed"))
plt = ggarrange(p1,p)
Warning: Removed 67 rows containing non-finite values (`stat_density()`).
Warning: Removed 1 rows containing missing values (`geom_segment()`).
plt

ggsave(filename = "./docs/Pseudotime_density_Lin1.svg", plt)
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ggsave(filename = "./docs/Pseudotime_denisty_Lin1.pdf", plt)
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p1 <-ggplot(dataset, aes(x=pseudotime_2))+
geom_density(aes(group=Genotype_Treatment, col=Genotype_Treatment), lwd=2)+
theme_classic()
p = ggplot(dataset, aes(x=UMAP_1, y=UMAP_2))+
geom_point(aes(col = pseudotime_2), size=4)+
geom_point(data=subset(dataset, dataset$Stroke_spatial==T), aes(fill=Stroke_spatial), pch=21)+
scale_fill_manual(values="black")+scale_color_viridis()+theme_classic()
# manually selected linage 3 here as this fits best to peudotimes values
p = p + geom_path(data = subset(curves, Lineage==2) %>% arrange(Order), aes(group = Lineage), lwd=2)+theme_classic()
p = p + geom_segment(data = curves_arrows[curves_arrows$Lineage==2,],aes(x = UMAP_1, y = UMAP_2, xend = xend, yend = yend), arrow = arrow(length=unit(0.3, "cm"), type="closed"))
plt = ggarrange(p1,p)
Warning: Removed 381 rows containing non-finite values (`stat_density()`).
Warning: Removed 1 rows containing missing values (`geom_segment()`).
plt

ggsave(filename = "./docs/Pseudotime_density_Lin2.svg", plt)
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ggsave(filename = "./docs/Pseudotime_denisty_Lin2.pdf", plt)
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GOplot <- function(gostresult)
if(is.null(gostresult)){
ggplot() + # Draw ggplot2 plot with text only
annotate("text",
x = 1,
y = 1,
size = 8,
label = "no significant GO-terms associated") +
theme_void()
} else {
gostplot(gostresult)}
sce_Pseudotime1 = samples.integrated[,samples.integrated$Celltype %in% linages$Lineage1]
res = comparison_rand(designform ="~1+Sex+Pseudotime_1",
Samples = c(),
log_cpm = log1p(as.data.frame(sce_Pseudotime1@assays$RNA@counts)),
samplesdata = as.data.frame(sce_Pseudotime1@meta.data),
randomeffect = c(),
target = "Pseudotime_1")
[1] "Sample length is 0 all samples included"
Warning: Zero sample variances detected, have been offset away from zero
analysis_sig <- res[res$adj.P.Val<0.05,]
analysis_sig %>% display_tab()
write.xlsx2(res, file ="./docs/DEX_Pseudotime_Lineage1.xlsx" , sheetName = "DEX genes Lin1")
ResGO = getGOresults(rownames(analysis_sig),
rownames(res),
"mmusculus")
GOplot(ResGO)
ResGO$result %>% display_tab()
sce_Pseudotime1@active.ident <- factor(x = sce_Pseudotime1@active.ident, levels = linages$Lineage1)
plt = DoHeatmap(sce_Pseudotime1, features = rownames(analysis_sig[order(analysis_sig$logFC),]))
Warning in DoHeatmap(sce_Pseudotime1, features = rownames(analysis_sig[order(analysis_sig$logFC), : The following features were omitted as they were not found in the scale.data slot for the SCT assay: Gm9644, Gm17661, Gm11760, Adh1, Gm15947, Tprg, Gm15168, Olfr732, Olig3, Gm10745, Gm44901, Gm8454, Gabrg1
plt

| Version | Author | Date |
|---|---|---|
| 9d29eff | Andreas Chiocchetti | 2023-08-11 |
ggsave(filename = "./docs/Pseudotime_Lin1_Marker.svg", plt)
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ggsave(filename = "./docs/Pseudotime_Lin1_Marker.pdf", plt)
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sce_Pseudotime2 = samples.integrated[,samples.integrated$Celltype %in% linages$Lineage2]
res = comparison_rand(designform ="~1+Sex+Pseudotime_2",
Samples = c(),
log_cpm = log1p(as.data.frame(sce_Pseudotime2@assays$RNA@counts)),
samplesdata = as.data.frame(sce_Pseudotime2@meta.data),
randomeffect = c(),
target = "Pseudotime_2")
[1] "Sample length is 0 all samples included"
Warning: Zero sample variances detected, have been offset away from zero
analysis_sig <- res[res$adj.P.Val<0.05,]
analysis_sig %>% display_tab()
write.xlsx2(res, file ="./docs/DEX_Pseudotime_Lineage2.xlsx" , sheetName = "DEX genes Lin1")
ResGO = getGOresults(rownames(analysis_sig),
rownames(res),
"mmusculus")
GOplot(ResGO)
ResGO$result %>% display_tab()
sce_Pseudotime2@active.ident <- factor(x = sce_Pseudotime2@active.ident, levels = linages$Lineage2)
plt = DoHeatmap(sce_Pseudotime2, features = rownames(analysis_sig[order(analysis_sig$logFC),]))
Warning in DoHeatmap(sce_Pseudotime2, features = rownames(analysis_sig[order(analysis_sig$logFC), : The following features were omitted as they were not found in the scale.data slot for the SCT assay: AC117656.2, AC135637.1, 9530053A07Rik, Vmn1r78, Olfr633, Ncapg, Olfr689, Gm20438, Tmem207, Gm12763, Gm37752, Sorcs1, 2700029L08Rik, Gm37970, Slc22a8, Gm7337, Cntnap5b, Gm29250, Olfr1406, Gm37087, Vmn1r217, Slc6a3, Gm26052, AC166056.1, Olfr721-ps1, Gm25100, Gm24975, CAAA01205117.2, Olfr307, Olfr1240, Gm37674, Gm28019, Gm26463, Gm44520, Gm8929, AC120394.2, Itgal, Cdh7, Mug-ps1, Pira2, Runx1t1, 4921534H16Rik, Spink5, Gm34004, Slc2a2, Glp2r, Pcdhb5, Il12b, Speer4cos, 5031434C07Rik, Gm43166, Gm30332, Olfr1, 1700121L16Rik, Efcab6, Gm22008, Gm22587, Olfr288, 4930445B16Rik, D630024D03Rik, Mro, Gm1979, Gm12315, Ciita, Olfr1500, Gm37368, Gm7950, Gm25789, Gm14291, Gm12657, 8430408G22Rik, Gm17661, Gm11760, Gm38093, Gm6634, Gm22852, Gm36388, Gm43690, Gm29170, Tmc8
plt

| Version | Author | Date |
|---|---|---|
| 9d29eff | Andreas Chiocchetti | 2023-08-11 |
ggsave(filename = "./docs/Pseudotime_Lin2_Marker.svg", plt)
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ggsave(filename = "./docs/Pseudotime_Lin2_Marker.pdf", plt)
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sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8 LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8 LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] gprofiler2_0.2.1 lm.beta_1.7-1 pheatmap_1.0.12 RColorBrewer_1.1-3 kableExtra_1.3.4 ggpubr_0.6.0 slingshot_2.6.0 TrajectoryUtils_1.6.0 SingleCellExperiment_1.20.0 princurve_2.1.6 DT_0.27 viridis_0.6.2 viridisLite_0.4.1 xlsx_0.6.5 brms_2.18.0 Rcpp_1.0.10 compareGroups_4.6.0 data.table_1.14.6 SingleR_2.0.0 SeuratObject_4.1.3 Seurat_4.3.0 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.1.8 ggplot2_3.4.1 tidyverse_1.3.2 DESeq2_1.38.3 SummarizedExperiment_1.28.0 Biobase_2.58.0 MatrixGenerics_1.10.0 matrixStats_0.63.0 GenomicRanges_1.50.2 GenomeInfoDb_1.34.9 IRanges_2.32.0 S4Vectors_0.36.1 BiocGenerics_0.44.0 limma_3.54.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rsvd_1.0.5 ica_1.0-3 svglite_2.1.1 ps_1.7.2 lmtest_0.9-40 rprojroot_2.0.3 crayon_1.5.2 MASS_7.3-58.2 nlme_3.1-162 backports_1.4.1 posterior_1.3.1 reprex_2.0.2 colourpicker_1.2.0 rlang_1.0.6 XVector_0.38.0 ROCR_1.0-11 readxl_1.4.2 irlba_2.3.5.1 callr_3.7.3 flextable_0.8.5 BiocParallel_1.32.5 bit64_4.0.5 glue_1.6.2 loo_2.5.1 sctransform_0.3.5 vipor_0.4.5 rstan_2.21.8 parallel_4.2.2 processx_3.8.0 spatstat.sparse_3.0-0 AnnotationDbi_1.60.0 spatstat.geom_3.0-6 haven_2.5.1 tidyselect_1.2.0 fitdistrplus_1.1-8 XML_3.99-0.13 zoo_1.8-11 packrat_0.9.0 distributional_0.3.1 chron_2.3-59 xtable_1.8-4 magrittr_2.0.3 evaluate_0.20 gdtools_0.3.0 cli_3.6.0 zlibbioc_1.44.0 rstudioapi_0.14 miniUI_0.1.1.1 sp_1.6-0 whisker_0.4.1 bslib_0.4.2 shinystan_2.6.0 shiny_1.7.4 BiocSingular_1.14.0 xfun_0.37 askpass_1.1 inline_0.3.19 pkgbuild_1.4.0 cluster_2.1.4 bridgesampling_1.1-2 gfonts_0.2.0 KEGGREST_1.38.0 Brobdingnag_1.2-9 ggrepel_0.9.3 threejs_0.3.3 listenv_0.9.0 xlsxjars_0.6.1 Biostrings_2.66.0 png_0.1-8 future_1.31.0 withr_2.5.0 bitops_1.0-7 plyr_1.8.8 cellranger_1.1.0 coda_0.19-4 pillar_1.8.1 RcppParallel_5.1.6 cachem_1.0.6 fs_1.6.1 DelayedMatrixStats_1.20.0 xts_0.12.2 vctrs_0.5.2 ellipsis_0.3.2 generics_0.1.3 dygraphs_1.1.1.6 tools_4.2.2 beeswarm_0.4.0 munsell_0.5.0 DelayedArray_0.24.0 fastmap_1.1.0 compiler_4.2.2 abind_1.4-5 httpuv_1.6.9 plotly_4.10.1 rJava_1.0-6 GenomeInfoDbData_1.2.9 gridExtra_2.3 lattice_0.20-45 deldir_1.0-6 utf8_1.2.3 later_1.3.0 jsonlite_1.8.4 scales_1.2.1 ScaledMatrix_1.6.0 carData_3.0-5 pbapply_1.7-0 sparseMatrixStats_1.10.0 lazyeval_0.2.2 promises_1.2.0.1 car_3.1-1 goftest_1.2-3 spatstat.utils_3.0-1 reticulate_1.28 checkmate_2.1.0 rmarkdown_2.20 cowplot_1.1.1 textshaping_0.3.6 webshot_0.5.4 Rtsne_0.16 uwot_0.1.14 igraph_1.4.0 rsconnect_0.8.29 survival_3.5-3 yaml_2.3.7 systemfonts_1.0.4 bayesplot_1.10.0 htmltools_0.5.4 rstantools_2.2.0 memoise_2.0.1 locfit_1.5-9.7 digest_0.6.31 assertthat_0.2.1 mime_0.12 RSQLite_2.2.20 future.apply_1.10.0 blob_1.2.3 ragg_1.2.5 labeling_0.4.2 shinythemes_1.2.0 splines_4.2.2 googledrive_2.0.0 RCurl_1.98-1.10 broom_1.0.3 hms_1.1.2 modelr_0.1.10 colorspace_2.1-0 base64enc_0.1-3 BiocManager_1.30.19 ggbeeswarm_0.7.1 ggrastr_1.0.1 nnet_7.3-18 sass_0.4.5 RANN_2.6.1 mvtnorm_1.1-3 fansi_1.0.4 tzdb_0.3.0 truncnorm_1.0-8 parallelly_1.34.0 R6_2.5.1 grid_4.2.2 crul_1.3 ggridges_0.5.4 lifecycle_1.0.3 StanHeaders_2.21.0-7 zip_2.2.2 writexl_1.4.2 curl_5.0.0 ggsignif_0.6.4 googlesheets4_1.0.1 leiden_0.4.3 jquerylib_0.1.4 Matrix_1.5-3 RcppAnnoy_0.0.20 spatstat.explore_3.0-6 htmlwidgets_1.6.1 officer_0.5.2 beachmat_2.14.0 polyclip_1.10-4 markdown_1.5 crosstalk_1.2.0 timechange_0.2.0 rvest_1.0.3 globals_0.16.2 openssl_2.0.5 patchwork_1.1.2 spatstat.random_3.1-3 tensorA_0.36.2 progressr_0.13.0 codetools_0.2-19 lubridate_1.9.2 gtools_3.9.4 getPass_0.2-2 prettyunits_1.1.1 dbplyr_2.3.0 gtable_0.3.1 DBI_1.1.3 git2r_0.31.0 highr_0.10 tensor_1.5 httr_1.4.4 KernSmooth_2.23-20 stringi_1.7.12 reshape2_1.4.4 farver_2.1.1 uuid_1.1-0 annotate_1.76.0 mice_3.15.0 xml2_1.3.3 shinyjs_2.1.0 geneplotter_1.76.0 scattermore_0.8 bit_4.0.5 spatstat.data_3.0-0 pkgconfig_2.0.3 gargle_1.3.0 rstatix_0.7.2 HardyWeinberg_1.7.5 Rsolnp_1.16 knitr_1.42 httpcode_0.3.0